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Record W3173628169 · doi:10.1080/14735784.2021.1929363

Baikonur 2.0: ‘inland-offshore’ space economies in post-Soviet Kazakhstan

2021· article· en· W3173628169 on OpenAlexfundno aff
Robert Kopack

Bibliographic record

VenueCulture, theory and critique/Culture, theory & critique · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaUniversity of TorontoAmerican Association of Geographers
KeywordsSoviet unionKazakhLeaseGeopoliticsSubmarine pipelinePolitical sciencePoliticsCold warEconomyBusinessGeologyLawEconomicsOceanography

Abstract

fetched live from OpenAlex

The global space industry brings to mind the horizons of science and technology, large rockets, and heroic astronauts. The land and infrastructure used to launch things into the cosmos, however, is far less seen. Since the mid-1950s, large territories or ‘fall zones’ in the Kazakh steppe have been used for jettisoning stages of inter-continental ballistic missiles and other kinds of carrier rockets from the Soviet launch complex, in the south west of the country, known as the Baikonur Cosmodrome. In this article, I explore how land leases and use agreements between the Russian Federation and Kazakhstan after the fall of the Soviet Union have upcycled this Soviet era site into a private enclave for the accumulation of capital and waste of a now global space industry. As during the Cold War, launches from Baikonur depend upon thousands of miles of downrange land in Kazakhstan to be catchment areas for toxic fuel and rocket debris that falls from the sky during each and every launch. Here, I introduce the concept of an ‘inland-offshore’, to explain how post-Soviet land and infrastructure lease agreements have created offshore-like political and economic privileges and extraterritorial landscapes of proprietary governance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.317
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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